Safe operation of intelligent robots in interactive environments depends on accurate prediction of others and consequent safe control of the ego robot. However, it remains challenging to 1) generate high-fidelity prediction of humans; 2) soundly verify the uncertainty associated with the prediction; and 3) incorporate the prediction and the verified uncertainty in the control of the ego robot. This project targets to address these three issues by incorporating recent progresses in 1) human motion prediction through imitation learning and online adaptation; 2) sound verification of deep neural networks; and 3) safe control of robot motion through the safe set algorithm. The work can be applied to human robot collaboration in production lines.

Sponsor: Ford Motor Company

Period of Performance: 2020 ~ 2022

Point of Contact: Ruixuan Liu